In this digital age, merchants are challenged by customers who want near instantaneous transactions across multiple channels and the need to reduce fraudulent transactions. At the same time, merchants struggle to enhance fraud detection for the rapidly growing market of digital goods.
With the prevalence of computers, mobile telephones and the Internet, buyers and sellers can now interact without seeing one another; card-not-present (CNP) transactions in which the merchant never sees the payment card are now much more common. As e-commerce becomes more popular and digital sales and delivery more common, merchants struggle to contain fraudulent payments without inconveniencing customers. A glitch during an e-commerce transaction can result in loss of the transaction for the merchant.
Unfortunately, card-not-present transactions—many of which are performed using computers, a telephone or mobile devices—are responsible for a great deal of fraud. Unfortunately, increasing fraud controls and tightening up fraud detection algorithms in order to detect fraud during a transaction can result in a good transaction being denied and a good customer being turned away. Most fraud detection systems are not yet able to handle the increase in online card-not-present transactions and especially the sale and delivery of digital goods.
No fraud detection system is perfect. Some amount of fraud will always occur in an on-line production system, resulting in chargebacks and losses for a merchant. Many systems attempt to address fraud during processing of a transaction through the use of fraud detection models that will not allow certain transactions. Indeed, transaction level fraud detection is known, making a decision about accepting or denying a transaction based upon some assessment of the risk level of that transaction. It is expected that a certain level of fraud will pass through the production system; this leads to a background fraud level that a merchant may find acceptable or can simply live with. Background fraud is characterized by multiple patterns without any single pattern dominating the fraud. By contrast, “special case” fraud occurs intermittently and is often part of a targeted attack by a fraudulent enterprise. Current techniques do not adequately address how to detect and prevent special case fraud. One reason is that prior art techniques look at transactions one at a time because of computational limits. Or, these techniques rely upon chargeback information which usually becomes available a few days after the transaction. This delay results in additional fraud losses because a fraudulent enterprise is able to exploit a weakness in the production system for a longer time.
Accordingly, new methods and systems are needed that allow a fraud detection system to detect and prevent special case fraud.